Vivian Yi-Ju Chen
Biographic Data
| ID | 1042754 |
|---|---|
| NAME | Vivian Yi-Ju Chen |
| GIVEN NAMES | Vivian Yi-Ju |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN V Y |
| AFFILIATIONS | Tamkang University |
| ORCID | 0000-0003-0116-9257 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 12 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 2 |
Geographically Weighted Regression Modeling for Multiple Outcomes
Geographically weighted regression (GWR) has been a popular tool applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such a technique, however, typically restricts the analysis to a single outcome variable and a set of explanatory variables. When analyzing multiple interrelated response variables, GWR fails to provide sufficient information about the data because it only allows separate modeling for each resp…
Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties
Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties
Geographically Weighted Regression Modeling for Multiple Outcomes
Geographically weighted regression (GWR) has been a popular tool applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such a technique, however, typically restricts the analysis to a single outcome variable and a set of explanatory variables. When analyzing multiple interrelated response variables, GWR fails to provide sufficient information about the data because it only allows separate modeling for each resp…
Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties
Geographically Weighted Regression Modeling for Multiple Outcomes
Geographically weighted regression (GWR) has been a popular tool applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such a technique, however, typically restricts the analysis to a single outcome variable and a set of explanatory variables. When analyzing multiple interrelated response variables, GWR fails to provide sufficient information about the data because it only allows separate modeling for each resp…
Econometrics (2 works) · Mathematics (2 works) · Regression analysis (2 works) · Statistics (2 works) · Computer Science (1 work) · Confounding (1 work) · Data mining (1 work) · Demography (1 work) · Demography (1 work) · Distribution (mathematics (1 work)